// head_to_head
Qwen3.7 Max vs SpaceXAI: Grok Build 0.1
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Qwen3.7 Max scores higher, SpaceXAI: Grok Build 0.1 costs less — it depends on your workload.
Qwen3.7 Max is ahead by 5.4 points overall, and SpaceXAI: Grok Build 0.1 lists 1.8× cheaper per blended million tokens. Whether 5.4 points is worth that depends on how much a wrong answer costs you. SpaceXAI: Grok Build 0.1 also leads on measured cost per point of capability, at $0.0144 per point.
qwen
Qwen3.7 Max
- Blended / 1M
- $2.21
- Context
- 1M
- Released
- May 21, 2026
- Overall score
- 73.1
x-ai
SpaceXAI: Grok Build 0.1
- Blended / 1M
- $1.25
- Context
- 256K
- Released
- May 20, 2026
- Overall score
- 67.8
Specs and pricing
| Metric | Qwen3.7 Max | SpaceXAI: Grok Build 0.1 |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 73.1win | 67.8 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0971 | $0.0144win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $2.21 | $1.25win |
| Input price / 1M | $1.48 | $1.00win |
| Output price / 1M | $4.42 | $2.00win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.295 | $0.200win |
| Context window | 1Mwin | 256K |
| Max output tokens | 131K | — |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Qwen3.7 Max on top, SpaceXAI: Grok Build 0.1 below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Qwen3.7 Max | SpaceXAI: Grok Build 0.1 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $623.04/mo | $342.40/mo |
| RAG assistant 8K in / 600 out × 100K requests | $973.50/mo | $600.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $873.20/mo | $512.00/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $1747.88/mo | $1110.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $3540.00/mo | $2300.00/mo |
Which should you pick?
You are running this at volume
SpaceXAI: Grok Build 0.1
Lowest measured cost per point of capability at $0.0144 per point — the gap compounds with every request.
Quality matters more than the bill
Qwen3.7 Max
Highest overall LiveBench score of the two at 73.1.
The workload is coding or agentic work
SpaceXAI: Grok Build 0.1
Leads on agentic coding — 45.8 against 43.6.
You need to fit large documents in one call
Qwen3.7 Max
Wider context window — 1M against 256K.
Qwen3.7 Max vs SpaceXAI: Grok Build 0.1 FAQ
Which is better, Qwen3.7 Max or SpaceXAI: Grok Build 0.1?
Qwen3.7 Max scores higher, SpaceXAI: Grok Build 0.1 costs less — it depends on your workload. Qwen3.7 Max is ahead by 5.4 points overall, and SpaceXAI: Grok Build 0.1 lists 1.8× cheaper per blended million tokens. Whether 5.4 points is worth that depends on how much a wrong answer costs you. SpaceXAI: Grok Build 0.1 also leads on measured cost per point of capability, at $0.0144 per point.
Is Qwen3.7 Max cheaper than SpaceXAI: Grok Build 0.1?
SpaceXAI: Grok Build 0.1 is cheaper. On a 3:1 input:output blend, Qwen3.7 Max lists at $2.21 per million tokens and SpaceXAI: Grok Build 0.1 at $1.25 — SpaceXAI: Grok Build 0.1 is 1.8× cheaper. Input and output are priced separately — Qwen3.7 Max charges $1.48 in and $4.42 out, SpaceXAI: Grok Build 0.1 charges $1.00 and $2.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Qwen3.7 Max vs SpaceXAI: Grok Build 0.1: which scores higher on benchmarks?
Qwen3.7 Max scores 73.1 and SpaceXAI: Grok Build 0.1 scores 67.8 overall on LiveBench, the mean of its seven categories. That is a 5.4-point lead for Qwen3.7 Max. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Qwen3.7 Max or SpaceXAI: Grok Build 0.1?
SpaceXAI: Grok Build 0.1. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Qwen3.7 Max works out at $0.0971 per point and SpaceXAI: Grok Build 0.1 at $0.0144.
Does Qwen3.7 Max or SpaceXAI: Grok Build 0.1 have a bigger context window?
Qwen3.7 Max has the larger context window: 1M for Qwen3.7 Max against 256K for SpaceXAI: Grok Build 0.1. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Qwen3.7 Max and SpaceXAI: Grok Build 0.1 support prompt caching?
Both publish a cached-input rate: $0.295 per million for Qwen3.7 Max and $0.200 for SpaceXAI: Grok Build 0.1, against full input rates of $1.48 and $1.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.